Dataset Information

Glycosyltransferase B4GALNT2 as a Predictor of Good Prognosis in Colon Cancer: Lessons from Databases.

ABSTRACT:

Background

glycosyltransferase B4GALNT2 and its cognate carbohydrate antigen Sd^a are highly expressed in normal colon but strongly downregulated in colorectal carcinoma (CRC). We previously showed that CRC patients expressing higher B4GALNT2 mRNA levels displayed longer survival. Forced B4GALNT2 expression reduced the malignancy and stemness of colon cancer cells.

Methods

Kaplan-Meier survival curves were determined in "The Cancer Genome Atlas" (TCGA) COAD cohort for several glycosyltransferases, oncogenes, and tumor suppressor genes. Whole expression data of coding genes as well as miRNA and methylation data for B4GALNT2 were downloaded from TCGA.

Results

the prognostic potential of B4GALNT2 was the best among the glycosyltransferases tested and better than that of many oncogenes and tumor suppressor genes; high B4GALNT2 expression was associated with a lower malignancy gene expression profile; differential methylation of an intronic B4GALNT2 gene position and miR-204-5p expression play major roles in B4GALNT2 regulation.

Conclusions

high B4GALNT2 expression is a strong predictor of good prognosis in CRC as a part of a wider molecular signature that includes ZG16, ITLN1, BEST2, and GUCA2B. Differential DNA methylation and miRNA expression contribute to regulating B4GALNT2 expression during colorectal carcinogenesis.

SUBMITTER: Pucci M

PROVIDER: S-EPMC8122605 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundColon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from pathological indicators. Transcription factors function as critical regulators in all aspects of cell life, but transcription factors-based biomarkers for colon cancer prognosis were still rare and necessary.MethodsWe implemented an innovative process to select the transcription factors variables and evaluate the prognostic prediction power by combining the Cox PH model with the random forest algorithm. We picked five top-ranked transcription factors and built a prediction model by using Cox PH regression. Using Kaplan-Meier analysis, we validated our predictive model on four independent publicly available datasets (GSE39582, GSE17536, GSE37892, and GSE17537) from the GEO database, consisting of 925 colon cancer patients.ResultsA five-transcription-factors based predictive model for colon cancer prognosis has been developed by using TCGA colon cancer patient data. Five transcription factors identified for the predictive model is HOXC9, ZNF556, HEYL, HOXC4 and HOXC6. The prediction power of the model is validated with four GEO datasets consisting of 1584 patient samples. Kaplan-Meier curve and log-rank tests were conducted on both training and validation datasets, the difference of overall survival time between predicted low and high-risk groups can be clearly observed. Gene set enrichment analysis was performed to further investigate the difference between low and high-risk groups in the gene pathway level. The biological meaning was interpreted. Overall, our results prove our prediction model has a strong prediction power on colon cancer prognosis.ConclusionsTranscription factors can be used to construct colon cancer prognostic signatures with strong prediction power. The variable selection process used in this study has the potential to be implemented in the prognostic signature discovery of other cancer types. Our five TF-based predictive model would help with understanding the hidden relationship between colon cancer patient survival and transcription factor activities. It will also provide more insights into the precision treatment of colon cancer patients from a genomic information perspective.